Apparatus and method for correcting error of bio-information sensor, and apparatus and method for estimating bio-information

ABSTRACT

An apparatus for providing corrected bio-information by using a bio-information sensor includes a communicator configured to receive bio-information from the bio-information sensor; a processor configured to extract metabolic information based on food intake information of a user and correct the received bio-information based on the extracted metabolic information; and an outputter configured to provide a result of correcting the bio-information.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This is a divisional application of U.S. application Ser. No.16/002,647, filed Jun. 7, 2018, which claims priority from Korean PatentApplication No. 10-2017-0134841, filed on Oct. 17, 2017, in the KoreanIntellectual Property Office, the entire disclosure of which isincorporated herein by reference in its entirety.

BACKGROUND 1. Field

Apparatuses and methods consistent with exemplary embodiments relate tocorrecting a measurement error of a bio-information measurement sensorand non-invasively estimating bio-information.

2. Description of Related Art

Diabetes is a chronic disease that causes various complications and canbe hardly cured, and hence blood glucose should be regularly monitoredto prevent complications. In addition, when insulin is administered, itis necessary to check blood glucose in an effort to avoid hypoglycemiaand control insulin dosage. Generally, an invasive method is used tomeasure blood glucose. The method of invasively measuring blood glucosemay provide high reliability in measurement, but it causes pain fromblood sampling, inconvenience and a risk of disease infection due to theuse of injection. Recently, methods of non-invasively estimating abiological component, such as blood glucose, through spectrum analysisby use of a spectrometer, without directly collecting blood, have beenstudied.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

According to an aspect of an exemplary embodiment, there is provided anapparatus of providing corrected bio-information by using abio-information sensor, the apparatus including: a communicatorconfigured to receive bio-information from the bio-information sensor; aprocessor configured to extract metabolic information based on foodintake information of a user and correct the received bio-informationbased on the extracted metabolic information; and an outputterconfigured to provide a result of correcting the bio-information.

The processor may acquire the food intake information based on at leastone from among food intake sensor information received from a foodintake sensor and information input by the user.

The processor may obtain a slope change of the bio-information based oncontinuous bio-information measurements included in the bio-informationreceived from the bio-information sensor and acquire the food intakeinformation based on the slope change.

The food intake information may include at least one from among a typeof food taken, an amount of the food taken, and a food intake time.

The metabolic information may include at least one from among a changeamount of the bio-information over time, a confidence interval of thebio-information, and a probability of the change amount being in acertain variation range.

The processor may extract the metabolic information by using at leastone from among a physiological metabolic model and a bio-informationdatabase.

The processor may correct a measurement of the received bio-informationby using at least one from among a correction formula and a correlationmodel of the measurement and the metabolic information.

The processor may determine that a measurement of the receivedbio-information which is out of a confidence interval of thebio-information as an outlier value, and correct the determined outliervalue to a value within the confidence interval.

The processor may correct a measurement of the received bio-informationthat is determined as not being based on an actual measurement of thebio-information, correcting the measurement being based on at least onefrom among a change amount of the bio-information over time and aprobability of the change amount being in a certain variation range.

The bio-information may include at least one from among blood glucose,cholesterol, triglycerides, protein, alcohol, and uric acid.

According to another aspect of an exemplary embodiment, there isprovided a method of providing corrected bio-information by using abio-information sensor, the method including: receiving bio-informationfrom the bio-information sensor; extracting metabolic information basedon food intake information of a user; and correcting the receivedbio-information based on the extracted metabolic information andproviding a result of correcting the bio-information.

The method may further include receiving food intake sensor informationfrom a food intake sensor; and acquiring the food intake informationbased on the received food intake sensor information.

The method may further include obtaining a slope change of thebio-information based on continuous measurements included in thereceived bio-information; and acquiring the food intake informationbased on the slope change.

The metabolic information may include at least one from among a changeamount of the bio-information over time, a confidence interval of thebio-information, and a probability of the change amount being in acertain variation range.

The extracting the metabolic information may include extracting themetabolic information by using at least one from among a physiologicalmetabolic model and a bio-information database.

The correcting the bio-information may include at least one from amongcorrecting an outlier value which is out of a confidence interval of thebio-information and correcting a measurement of the receivedbio-information that is determined as not being based on an actualmeasurement of the bio-information.

According to still another aspect of an exemplary embodiment, there isprovided an apparatus for estimating bio-information, including: asensor configured to obtain sensor information from a user; and aprocessor configured to estimate the bio-information based on the sensorinformation, extract metabolic information based on food intakeinformation of the user, correct the estimated bio-information using theextracted metabolic information, and provide a result of correcting theestimated bio-information.

The sensor information may include at least one from among spectrometermeasurement information, impedance measurement information, ultrasonicmeasurement information, thermal measurement information,electrocardiography (ECG) information, electroencephalogram (EEG)information, electromyography (EMG) information, electrooculography(EOG) information, and photoplethysmography (PPG) information.

The sensor may include a food intake sensor configured to acquire foodintake sensor information by detecting a food intake of the user, andthe processor is further configured to acquire the food intakeinformation based on the food intake sensor information.

The processor may extract the metabolic information by using at leastone from among a physiological metabolic model and a bio-informationdatabase.

The metabolic information may include at least one from among a changeamount of the bio-information over time, a confidence interval of thebio-information, and a probability of the change amount being in acertain variation range.

The processor may correct an estimate of the bio-information by using atleast one from among a correlation model and a correction formulaindicating a correlation between the estimate of the bio-information andthe change amount of the bio-information over time or the probability ofthe change amount being in the certain variation range.

The processor may correct an outlier value among estimates of thebio-information which is out of the confidence interval with a valuewithin the confidence interval.

The processor may determine an estimate of the bio-information as notbeing based on an actual measurement of the bio-information and correctthe estimate based on at least one from among the change amount of thebio-information over time and the probability of the change amount ofthe bio-information being in the certain variation range.

According to still another aspect of an exemplary embodiment, there isprovided a method of estimating bio-information, including: obtainingsensor information from a user; estimating bio-information based on thesensor information; acquiring food intake information of the user;extracting metabolic information based on the food intake information;and correcting the estimated bio-information based on the extractedmetabolic information and providing a result of correcting the estimatedbio-information.

The sensor information may include at least one from among spectrometermeasurement information, impedance measurement information, ultrasonicmeasurement information, thermal measurement information, ECGinformation, EEG information, EMG information, EOG information, and PPGinformation.

The obtaining the sensor information may include obtaining food intakesensor information by using a food intake sensor configured to detect afood intake of the user and the acquiring the food intake informationincludes acquiring the food intake information of the user based on thefood intake sensor information.

The extracting the metabolic information may include extracting themetabolic information by using at least one from among a physiologicalmetabolic model and a bio-information database.

The correcting the bio-information may include at least one from amongcorrecting an outlier value which is out of a confidence interval of thebio-information and correcting an estimate of the receivedbio-information that is determined as not being based on an actualmeasurement of the bio-information.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or other aspects of the disclosure will become apparentand more readily appreciated from the following description of theexemplary embodiments, taken in conjunction with the accompanyingdrawings.

FIG. 1 is a block diagram illustrating a bio-information measurementsystem according to an exemplary embodiment.

FIG. 2 is a block diagram illustrating an apparatus of correcting anerror of a bio-information sensor according to an exemplary embodiment.

FIG. 3 is a block diagram illustrating a configuration of a processoraccording to an exemplary embodiment of FIG. 2.

FIG. 4 is a diagram for describing an exemplary embodiment of acquiringintake information.

FIGS. 5A to 5D are diagrams for describing exemplary embodiments ofextracting metabolic information.

FIGS. 6A to 6D are graphs for describing exemplary embodiments ofcorrecting an error of bio-information.

FIG. 7 is a flowchart illustrating a method of correcting an error of abio-information sensor according to an exemplary embodiment.

FIG. 8 is a block diagram illustrating an apparatus for estimatingbio-information according to an exemplary embodiment.

FIG. 9 is a diagram illustrating a wearable device according to anexemplary embodiment.

FIG. 10 is a flowchart illustrating a method of estimatingbio-information according to an exemplary embodiment.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses and/orsystems described herein. Various changes, modifications, andequivalents of the systems, apparatuses and/or methods described hereinwill suggest themselves to those of ordinary skill in the art. In thefollowing description, a detailed description of known functions andconfigurations incorporated herein will be omitted when it may obscurethe subject matter with unnecessary detail.

Hereinafter, exemplary embodiments will be described in detail withreference to the drawings. Throughout the drawings and the detaileddescription, unless otherwise described, the same drawing referencenumerals will be understood to refer to the same elements, features, andstructures. The relative size and depiction of these elements may beexaggerated for clarity, illustration, and convenience.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. Also, the singular forms are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. In the specification, unless explicitly described to thecontrary, the word “comprise” and variations such as “comprises” or“comprising”, will be understood to imply the inclusion of statedelements but not the exclusion of any other elements. Terms such as “ .. . unit” and “module” denote units that process at least one functionor operation, and they may be implemented by using hardware, software,or a combination of hardware and software.

FIG. 1 is a block diagram illustrating a bio-information measurementsystem according to an exemplary embodiment.

Referring to FIG. 1, a bio-information measurement system 1 includes abio-information sensor 110, a food intake sensor 120, and an errorcorrection apparatus 200.

The bio-information sensor 110 measures bio-information from a user. Thebio-information sensor 110 may be attached to or worn on a part to beinspected to measure the bio-information from the corresponding part.The bio-information sensor 110 may include a sensor configured tocontinuously measure bio-information at predetermined time intervals(e.g., 5 minutes, 10 minutes, 15 minutes, etc.), for example, acontinuous blood glucose measurement sensor. In this case, thebio-information sensor 110 may be a non-invasive sensor that measuresthe bio-information based on sensor information obtained from varioussensors, for example, spectrometer measurement information, such asspectral information, impedance measurement information, ultrasonicmeasurement information, thermal measurement information,electrocardiography (ECG) information, electroencephalogram (EEG)information, electromyography (EMG) information, electrooculography(EOG) information, and photoplethysmography (PPG) information. However,the type of sensor is not limited to the above examples. For example,the bio-information sensor may include an invasive or minimally invasivesensor. The bio-information may include, but is not limited to, one ormore of blood glucose, cholesterol, triglycerides, protein, alcohol, anduric acid.

The food intake sensor 120 may detect the user's food intake andgenerate food intake sensor information. The food intake sensor 120 maybe provided as a separate hardware device to be attached to or worn on abody part of the user. Alternatively, the food intake sensor 120 may bemounted in the bio-information sensor 110 or the error correctionapparatus 200. For example, the food intake sensor 120 may include asensor that is worn on a user's ear and detects the user's food intakesounds, a gyro sensor that is worn on a user's wrist and detects auser's arm motion, a sensor that detects a user's chest movement orrespiratory, a sensor that senses food that the user is eating, apiezoelectric sensor that detects the user's swallowing or musclemovement of a neck, and the like. Alternatively, the food intake sensor120 may include a camera module and the like which acquires imageinformation related to the user's food intake.

The error correction apparatus 200 may receive continuousbio-information measurements from the bio-information sensor 110. Whenthe error correction apparatus 200 receives the bio-information from thebio-information sensor 110, the error correction apparatus 200 maycorrect the bio-information based on the user's food intake informationand output the correction result as final bio-information. In this case,the food intake information may include one or more of an intake food, afood intake amount, and a food intake time.

FIG. 2 is a block diagram illustrating an apparatus of correcting anerror of a bio-information sensor according to an exemplary embodiment.FIG. 3 is a block diagram illustrating a configuration of a processoraccording to an exemplary embodiment of FIG. 2. FIG. 4 is a diagram fordescribing an exemplary embodiment of acquiring intake information.

The error correction apparatus 200 according to an exemplary embodimentmay be a hardware device physically independent of (or separate from)the bio-information sensor 110. For example, the error correctionapparatus 200 which is an information processing device, such as asmartphone, a tablet personal computer (PC), a notebook PC, a desktopPC, and a server, is not particularly limited in terms of portabilityand size, and may be provided in various forms according to theapplication purpose of the error correction apparatus 200.

Referring to FIG. 2, the error correction apparatus 200 includes aprocessor 210, a communicator 220, an inputter 230, an outputter 240,and a storage 250.

The communicator 220 may communicate with an external device, includingthe bio-information sensor 110, by using a communication technologyunder the control of the processor 210, and may transmit and receivevarious items of data. For example, the communicator 220 may receive abio-information measurement from the bio-information sensor 110 at apredetermined time interval and transmit the received measurement to theprocessor 210, and also may transmit a processing result of theprocessor 210 to the external device. In this case, the external devicemay be a user's device providing excellent computing performance, suchas a smartphone, a tablet PC, a desktop PC, or a notebook PC, or acomputing medical device in a medical institution.

The communication technology may include a Bluetooth communication, aBluetooth low energy communication, a near-field communication (NFC), awireless local area network (WLAN) communication, a ultra-wideband (UWB)communication, an Ant+communication, a Wi-Fi communication, and a mobilecommunication, but is not limited thereto.

In addition, the communicator 220 may communicate with the food intakesensor 120 under the control of the processor 210, receive the foodintake sensor information and transmit the received information to theprocessor 210.

The inputter 230 may receive a variety of information including the foodintake information from the user and transmit the information to theprocessor 210. The inputter 230 may output a user interface to a displayand receive a variety of information input by the user through the userinterface. Alternatively, when the error correction apparatus 200employs a voice recognition technology, the inputter 230 may receivevoice information input by the user. However, exemplary embodiments arenot limited thereto, and the inputter 230 may be variously embodied inaccordance with user input methods. For example, the inputter 230 mayinclude a keyboard, a virtual keyboard on a touch screen, a remotecontrol signal receiver for receiving a remote control signalcorresponding to a user input from a remote controller, a camera forsensing a user gesture input, a microphone for receiving a user's voiceinput, etc.

The processor 210 may receive the bio-information from thebio-information sensor 110 through the communicator 220 and correct thebio-information based on the food intake information.

Referring to FIG. 3, the processor 210 includes a food intakeinformation acquirer 211, a metabolic information extractor 212, and acorrector 213.

The food intake information acquirer 211 may acquire food intakeinformation by analyzing the food intake sensor information receivedfrom the food intake sensor 120 or continuous bio-informationmeasurements received from the bio-information sensor 110.Alternatively, the food intake information acquirer 211 may acquire foodintake information directly input by the user through an interface.

For example, when the food intake information acquirer 211 receives thefood intake sensor information, such as a food intake sound, a capturedfood image, information of swallowing detection, information of armmovement detection, or the like, the food intake information acquirer211 may analyze the received food intake sensor information to acquirefood intake information, such as a type and an amount of food taken bythe user, or the food intake time.

In another example, the food intake information acquirer 211 may obtaina slope change of the bio-information by analyzing the continuousbio-information measurements received from the bio-information sensor110 and, and acquire the food intake information based on the slopechange. FIG. 4 is a graph showing an exemplary embodiment of continuousblood glucose measurements received from a blood glucose measurementsensor. In FIG. 4, an abrupt slope change of the blood glucose level isshown from around 9:10 to around 9:40. The food intake informationacquirer 211 may determine that a point in time at which the slopechange of the blood glucose level is drastic is when the user took thefood. In addition, the food intake information acquirer 211 may estimatethe type or amount of food that the user has eaten based on informationpredefined for the user indicating a correlation between a change inblood glucose level and food information.

The metabolic information extractor 212 may extract metabolicinformation based on the food intake information. In this case, themetabolic information may include a change amount of bio-informationwith respect to time, a confidence interval of the bio-information, anda probability or frequency of each variation range of thebio-information.

FIGS. 5A to 5D are diagrams for describing exemplary embodiments ofextracting metabolic information. An exemplary embodiment in which themetabolic information extractor 212 extracts metabolic information willbe described with reference to FIG. 2 and FIGS. 5A to 5D.

For example, when the food intake information of the user is acquired,the metabolic information extractor 212 may extract a change amount ofbio-information with respect to time using a physiological metabolicmodel according to a transfer of a substance related to thebio-information among organs in a human body.

FIG. 5A illustrates an example of a metabolic model in which bloodglucose metabolism according to the substance transfer between organ 1(e.g., stomach) and organ 2 (e.g., intestine) in the human body ismathematized. In the example metabolic model shown in FIG. 5A, K₁₀represents a substance transfer constant when the substance isdischarged from organ 1, K₁₂ represents a substance transfer constantwhen the substance is transferred from organ 1 to organ 2, and K₂₁represents a substance transfer constant when the substance istransferred from organ 2 to organ 1. C₁ and C₂ represent blood glucoseconcentrations in organ 1 and organ 2, respectively, and dC₁ and dC₂represent variations in blood glucose level in organ 1 and organ 2,respectively. The physiological metabolic model may be personalized bymodeling various factors of individual users related to internalabsorption or distribution of substances, metabolism of organs, such asa liver and a stomach, exertion, and the like.

FIG. 5B is an example of estimating a change amount over time of bloodglucose level which is estimated using a physiological metabolic modelwhen user's food intake information is given, for example, when the usertakes 75 grams of sugar.

In another example, the metabolic information extractor 212 may extracta confidence interval of the bio-information, or probability orfrequency information of a variation range of the bio-informationaccording to time with respect to the food intake information byreferring to a bio-information database established in advance. Thebio-information database may include time-based bio-informationmeasurements according to the type or amount of food eaten related tothe bio-information for each user. Referring to FIG. 5C, the metabolicinformation extractor 212 may extract a confidence interval according totime, for example, a minimum value CI_(min) and a maximum valueCI_(max). FIG. 5D shows a probability of a change amount over time of ablood glucose level being in each variation range, which is extracted bythe metabolic information extractor 212 by referring to thebio-information database.

When the metabolic information extractor 212 extracts the information,such as a change amount over time of bio-information, the confidenceinterval, or a probability of each variation range, the corrector 213may correct the measurement values, outlier values or missing values ofbio-information received from the bio-information sensor 110 using themetabolic information.

FIGS. 6A to 6D are graphs for describing exemplary embodiments ofcorrecting an error of bio-information. Exemplary embodiments in whichthe corrector 213 corrects the bio-information received from thebio-information sensor 110 will be described with reference to FIGS. 6Ato 6D.

In one example, the corrector 213 may correct the bio-informationmeasurement measured by the bio-information sensor 110 using acorrection formula of the bio-information or a correlation model. Thecorrelation model may be provided in advance through linear regressionmodeling or machine learning modeling a relationship among thebio-information measurement obtained by the bio-information sensor, achange amount over time of bio-information in the metabolic information,and an actual bio-information measurement obtained through bloodcollection. In addition, the correction formula of the bio-informationmay be a linear function predefined as shown in Equation 1 or Equation 2below. However, Equation 1 and Equation 2 are merely examples andexemplary embodiments are not limited thereto.

G _(t) =w ₁ M _(t) +w ₂ N _(t)  (1)

G _(t) =N _(t) −w ₁ M ₁  (2)

Equation 1 is an example in which correction is performed by applying aweight to the bio-information measurement N_(t) and a bio-informationvariation M_(t) at a specific time point t and summing the weightedvalues. Equation 2 is an example in which correction is performed bysubtracting a weighted bio-information variation M_(t) from thebio-information measurement N_(t) at a specific time point t. Here, w₁and w₂ represent weights applied to the bio-information measurementN_(t) and the bio-information variation M_(t), respectively, at aspecific time point t, and G_(t) represents a correction result at thespecific time point t.

FIG. 6A shows a result of comparing actual blood glucose measurements 61obtained through blood collection with a correction result 62 a obtainedby correcting the blood glucose measurements measured by a blood glucosesensor using the above Equation 1, and a result of comparing the actualblood glucose measurements 61 with blood measurements 62 b correctedusing a related art smoothing method. As shown in the graphs of FIG. 6A,it can be seen that the correction result 62 a of the blood glucosemeasurements according to an exemplary embodiment is more similar to theactual blood glucose measurements 61 obtained through blood collectionthan the blood glucose measurements 62 b obtained by the related artsmoothing method.

In another example, the corrector 213 may correct an outlier value amongthe bio-information measurements measured by the bio-information sensor110. The corrector 213 may determine an outlier value among thebio-information measurements based on confidence interval information ofthe bio-information extracted by the metabolic information extractor211. For example, when a bio-information measurement at a specific pointin time is out of the confident interval shown in FIG. 5C, for example,when the bio-information measurement is less than the minimum valueCI_(min) or greater than the maximum value CI_(max) at the same point intime, the corrector 213 may determine that the bio-informationmeasurement value is an outlier value.

When the bio-information measurement at the specific point in time isdetermined as an outlier value, the corrector 213 may correct thedetermined outlier value by replacing the outlier value with anarbitrary value within the confidence interval at the same point intime. For example, the corrector 213 may replace the outlier value witha boundary value of the confidence interval at the same point in time.In other words, when the outlier value is less than the minimum valueCI_(min) of the confidence interval, the outlier value may be replacedwith the minimum value CI_(min), and when the outlier value is greaterthan the maximum value CI_(max) of the confidence interval, the outliervalue may be replaced with the maximum value CI_(max). Alternatively,the corrector 213 may correct the outlier value by subtracting aweighted bio-information variation from the outlier value, as shown inthe above Equation 2, such that the outlier value becomes a value withinthe confidence interval. When the outlier value of the bio-informationis corrected, the corrector 213 may smooth the correctedbio-information.

FIG. 6B illustrates graphs showing a result of comparing actual bloodglucose measurements 63 obtained through blood collection with bloodglucose measurements 64 a received from the bio-information sensor 110and a result of comparing the actual blood glucose measurements 63 withmeasurements 64 b after correcting outlier values. Outlier values OL1and OL2 that are out of a confidence interval are present in the bloodglucose measurements 64 a received from the bio-information sensor 110.By correcting the outlier values OL1 and OL2 that are out of theconfidence interval to be boundary values CV1 and CV2 of the confidenceinterval, the outlier values may become similar to the actual bloodglucose measurements 63.

In another example, the corrector 213 may correct a missing value of thebio-information measured by the bio-information sensor 110. Here,correcting the missing value may mean replacing a point in time or atime interval of the received measurements with a corrected value(s)when it is determined that the bio-information measurement is notactually performed at the point in time or in the time interval. Forexample, the corrector 213 may analyze the measurements received fromthe bio-information sensor 110 and estimate a missing value of thebio-information measurement. Here, estimating the missing value may meandetermining a missing interval or a point in time where the actualbio-information measurement is not performed, based on a result ofanalyzing the bio-information measurements. For example, the missingvalue may be estimated based on continuous bio-information measurementsreceived from the bio-information sensor 110. The missing value mayoccur in various situations such as, for example, when the user does notwear the bio-information sensor 110 for a certain period of time, whenthe power is turned off, or when the bio-information sensor 110 is inpoor contact with the user's body part to be inspected.

When the missing value is estimated, the corrector 213 may correct themissing value using the bio-information variation or the probabilityinformation of each variation range extracted by the extractor 212. Forexample, the corrector 213 may correct a missing value N_(t) at aspecific time point t using a bio-information variation M_(t) at thesame time point t through Equation 3 below. However, Equation 3 ismerely an example, and the missing value N_(t) may be corrected by usingvarious methods, such as, for example, replacing the missing value N_(t)with the bio-information variation M_(t) at the same time point t,replacing the missing value N_(t) with an average or an intermediatevalue of bio-information variations (M_(t−1), M_(t+1)) at the precedingand subsequent time points (e.g., t−1 and t+1) of the specific timepoint t, and the like.

G _(t) =N ₁+(M _(t) −M ₁)  (3)

Here, N₁ represents a bio-information measurement at an initial timepoint (t=1), and M₁ represents a bio-information variation at theinitial time point (t=1). In addition, G_(t) represents a result ofcorrecting a missing value at a specific time point t.

In addition, the corrector 213 may correct the missing value based onTable 1 below. Table 1 shows counts of range transitions according to apredetermined time before a missing time point t based onbio-information probability information for each variation range of FIG.5. In Table 1, G_(t−3), G_(t−2), G_(t−1), and G_(t) representbio-information measurements or corrected values at time points t−3,t−2, t−1, and t, respectively. For example, when a bio-informationmeasurement N_(t−3) is present at a specific time point t−3 and is notan outlier value, G_(t−3) may be the bio-information measurement N_(t−3)or a corrected value obtained by the above Equation 1 or 2. When thebio-information measurement N_(t−3) is an outlier value or a missingvalue, G_(t−3) may be a corrected value obtained by correcting theoutlier value or the missing value as described above.

TABLE 1 G_(t−2) − G_(t−3) G_(t−1) − G_(t−2) G_(t−1) − G_(t) Count 0~+100~+10 −30~−20 1 0~+10 0~+10 −20~−10 5 0~+10 0~+10 −10~0  22 0~+10 0~+10 0~+10 67 0~+10 0~+10 +10~+20 18 0~+10 0~+10 +20~+30 2

Referring to Table 1, when bio-information transition from G_(t−2) toG_(t−3) in a first interval (t−3 to t−2) and bio-information transitionfrom G_(t−1)-G_(t−2) in a second interval (t−2 to t−1) fall in a rangeof 0 to +10, bio-information transition from G_(t−1) to G_(t) in aninterval from t−1 to a current time point t is most likely to fall in arange of 0 to +10. Therefore, the corrector 213 may correct a missingvalue N_(t) by adding a constant k to a bio-information value G_(t−1) ofthe preceding time point t−1, as shown in Equation 4 below. Here, k maybe an arbitrary number (e.g., an intermediate value of 5) which allowstransition from the preceding time point t−1 to the current time point tto have a range of 0 to +10.

G _(t) =G _(t−1) +k  (4)

FIG. 6C shows a blood glucose measurement result 66 when the user doesnot wear the bio-information sensor 110 or the bio-information is notmeasured for a considerable amount of time, e.g., due to power off ofthe bio-information sensor 110. In this case, an interval MR from about25 minutes to about 80 minutes represents a missing interval where ameasurement is not actually performed. The corrector 213 may estimatethe missing interval as a missing value and correct (e.g., replace) themissing value. Values of points indicated by circles in the bloodglucose measurement result 66 in FIG. 6C indicate bio-informationmeasurements or corrected values obtained by correcting biometricinformation measurements, and values at points indicated by rectanglesindicate corrected values for missing values. It can be seen that thewhole blood glucose measurement result 66 after the correction of themissing values becomes very similar to blood glucose levels 65 that areactually measured through blood collection.

FIG. 6D shows an example in which momentary missing values occur due toa poor contact between the bio-information sensor 110 and the part to beinspected even when the user is wearing the bio-information sensor 110.Similarly to FIG. 6C, values of points indicated by circles in a bloodglucose measurement result 67 in FIG. 6D indicate the bio-informationmeasurements or corrected values obtained by correcting themeasurements, and values of points indicated by rectangles indicatecorrected values obtained by correcting missing values.

Referring back to FIG. 2, the outputter 240 may provide a processingresult of the processor 210 to the user. For example, the outputter 240may visually output a current bio-information correction result as afinal bio-information value to the display, or may output it in anaudible manner, such as voice. In addition, the continuousbio-information measurements received from the bio-information sensor110 or a result of correcting the continuous measurements may be outputin the form of a graph. However, exemplary embodiments are not limitedthereto, and the outputter 240 may be variously embodied in accordancewith output methods. For example, the outputter 240 may output an audiosignal and/or a video signal and may include the display and/or an audiooutput device, e.g., a speaker, audio jack, audio output device, etc.).

A variety of reference information to be used for bio-informationmeasurement and correction may be stored in the storage 250. Forexample, the reference information may include user information, such asthe age, sex and health status of the user, and information about theaforementioned physiological metabolic model, a bio-informationdatabase, a bio-information correction formula, and a correlation model.In addition, the food intake information and the metabolic informationacquired by the processor 210 and the continuous bio-informationmeasurements and the bio-information correction result received from thebio-information sensor 110 may be stored. The storage 250 may include,but not limited to, one or more types of storage media including a flashmemory, a hard disk, a multimedia card micro type memory, a card-typememory (e.g., secure digital (SD) or eXtreme digital (XD) memory, etc.)a random access memory (RAM), a static random access memory (SRAM), aread-only memory (ROM), an electrically erasable programmable read-onlymemory (EEPROM), a programmable read-only memory (PROM), a magneticmemory, a magnetic disk, and an optical disk,

According to exemplary embodiments, when the bio-information is measuredby a bio-information sensor in a non-invasive manner, it is possible toaccurately measure the bio-information by correcting the bio-informationmeasurements, the outlier values and the missing values using the user'smetabolic information. Therefore, the solutions according to exemplaryembodiments solve the problems in that conventional non-invasive methodsof estimating a biological component are not accurate as invasivemethods and require monitoring and/or removal of a noise.

FIG. 7 is a flowchart illustrating a method of correcting an error of abio-information sensor according to an exemplary embodiment.

The method shown in FIG. 7 is an exemplary embodiment of an errorcorrection method performed by the error correction apparatus of thebio-information sensor in accordance with an exemplary embodiment ofFIG. 2. Various exemplary embodiments of the error correction methodperformed by the error correction apparatus 200 have been described, andthus will be described in brief hereafter.

The error correction apparatus 200 receives bio-information measured bythe bio-information sensor in operation 710. In this case, thebio-information sensor may be a continuous measurement sensor thatmeasures the user's bio-information continuously.

The error correction apparatus 200 acquires the user's food intakeinformation in operation 720. In this case, the food intake informationmay include a type and an amount of food eaten, the time of food intake,and the like. For example, the error correction apparatus 200 mayacquire food intake sensor information about the user's food intake fromthe food intake sensor, and acquire the food intake information based onthe acquired food intake sensor information. In another example, theerror correction apparatus 200 may obtain a slope change of thebio-information by analyzing the continuous bio-information measurementsreceived in operation 710 and determine that a time at which the slopechange is abrupt is when the user takes food. In another example, theuser may input food intake information, such as a type and an amount offood taken by the user, the food intake time, and the like, through aninterface.

The error correction apparatus 200 extracts metabolic informationrelated to the bio-information based on the food intake information inoperation 730. In this case, the metabolic information may include achange amount of bio-information with respect to time, a confidenceinterval of the bio-information, and probability information of eachvariation range of the bio-information. The error correction apparatus200 may extract a change amount of bio-information according to theuser's food intake using a physiological metabolic model personalized toeach user. Alternatively, the error correction apparatus 200 may extracta change amount and a confidence interval of the bio-information,probability information of each variation range of the bio-information,and the like by using a bio-information database.

The error correction apparatus 200 corrects the bio-information measuredby the bio-information sensor based on the extracted metabolicinformation in operation 740. For example, the error correctionapparatus 200 may correct continuous bio-information measurementsmeasured based on the change amount of the bio-information. In addition,the error correction apparatus 200 may determine an outlier value amongthe continuous bio-information measurements based on the confidenceinterval of the bio-information, and replace the determined outliervalue with a value in the confidence interval. Further, the errorcorrection apparatus 200 may estimate a missing value by analyzing thecontinuous bio-information measurements and correct the estimated valuebased on the probability information of each variation range of thebio-information.

The error correction apparatus 200 outputs a result of correcting thebio-information in operation 750. At this time, the error correctionapparatus may visually output the bio-information correction result bydisplaying the bio-information correction result to a display, or mayconvert the bio-information correct result into a voice signal andaudibly output the voice signal to the user through a speaker module.

FIG. 8 is a block diagram illustrating an apparatus 800 for estimatingbio-information according to an exemplary embodiment.

The apparatus 800 for estimating bio-information according to anexemplary embodiment may be an apparatus that non-invasively estimates avariety of bio-information including blood glucose, cholesterol,triglycerides, protein, alcohol, and uric acid. According to anexemplary embodiment, the apparatus 800 may be implemented as awrist-watch-type wearable device, as shown in FIG. 9. However, exemplaryembodiments are not limited thereto, and the wearable device may be ofany other types, for example, a bracelet-type, a wristband type, a ringtype, a glasses type, a hairband type, or the like and may be providedin various sizes and forms depending on the purpose of bio-informationestimation or the place where the bio-information estimation apparatusis used. Alternatively, the apparatus 800 may be mounted in aninformation processing device, such as a smartphone, a tablet PC, or thelike.

Referring to FIG. 8, the apparatus 800 includes a processor 810, asensor 820, and an outputter 830.

The sensor 820 may collect sensor information from a user under thecontrol of the processor 810. For example, the sensor 810 may include anon-invasive sensor, such as a spectroscopic sensor, an impedancesensor, an ultrasonic sensor, a thermal sensor, an ECG sensor, an EEGsensor, an EMG sensor, an EOG sensor, or a PPG sensor, and collectsensor information, such as spectral information, impedance measurementinformation, ultrasound measurement information, thermal measurementinformation, ECG information, EEG information, EMG information, EOGinformation, or PPG information. Hereinafter, for convenience ofdescription, a case in which the sensor 820 includes a spectroscopicsensor and spectral information is acquired through the spectroscopicsensor as sensor information will be taken as an example.

The spectroscopic sensor may include a light source configured to emitlight onto a body part of the user to be inspected and a detectorconfigured to detect light scattered or reflected back from theirradiated body part after the emitted light has been absorbed by thetissue of the body part. In this case, the light source may be a lightemitting diode (LED), a laser diode, a phosphor, and the like. The lightsource may be configured to emit near infrared light, but is not limitedthereto, and may emit a single laser beam. In addition, the detector mayinclude a photodiode.

In addition, the sensor 820 may include a food intake sensor configuredto collect food intake sensor information by detecting the user's foodintake. The food intake sensor may be a sensor configured to recognizethe sound of food intake or swallowing, or a muscle movement of theuser, or capture an image of food that the user is eating. The foodintake sensor may be mounted in one body structure along with the otherconfigurations, such as the spectroscopic sensor, the processor 810, andthe outputter 830, but is not limited thereto. For example, the foodintake sensor may be implemented as a separate device to be worn on abody part (e.g., an ear, a wrist, etc.) of the user, and transmit foodintake sensor information to the processor 810 via a wired and/orwireless communication through a communication module mounted in theapparatus 800.

The processor 810 may control the sensor 820 when the processor 810receives a request for estimating bio-information, and may receivesensor information from the sensor 820. The processor 810 may estimatebio-information by using the sensor information received from the sensor810 and provide an estimation result to the user through the outputter830. For example, the processor 810 may include a central processingunit (CPU).

When the processor 810 receives the request for estimatingbio-information from the user or an external device, the processor 810may drive the light source to emit light to the body part of the user tobe inspected by controlling the spectroscopic sensor. In addition, whenspectrum information is received from the detector, the processor 810may estimate bio-information by applying a bio-information estimationmodel. In this case, the bio-information estimation model may be alinear function that represents a correlation between a spectrum and thebio-information.

The processor 810 may acquire food intake information related to thebio-information when the user has eaten food. In this case, the foodintake information may include a type or an amount of food that the userhas eaten, time of food intake, and the like. In one example, when thefood intake sensor information is received from the food intake sensor,the food intake information may be acquired by analyzing the receivedfood intake sensor information. In another example, the processor 810may provide a user interface to the user through the outputter 830 andreceive food intake information input by the user through the interface.In still another example, the processor 810 may monitor continuousbio-information estimates, and determine that a time interval in which aslope change of the continuous bio-information estimates that ismonitored is greater than a threshold range is the time when the usertakes food. In addition, a type or an amount of food that has been eatenmay be estimated according to the bio-information estimates and a slopechange of the bio-information estimates by referring to abio-information database personalized to each user. However, exemplaryembodiments are not limited to the above examples. For example, the foodintake information may be acquired by combining results of the above twoor more examples or using other pieces of information which have notbeen illustrated.

When the user's food intake information is acquired, the processor 810may extract metabolic information using the acquired food intakeinformation. The metabolic information may include a change amount overtime of bio-information, a confidence interval, probability informationof each variation range, or the like. For example, as described above,the processor 810 may extract the metabolic information using aphysiological metabolic model or a bio-information database which isprovided in advance and personalized to the user according to the typeand amount of food that the user has eaten, and the time of food intake.

When the metabolic information is extracted, the processor 810 maycorrect bio-information estimated based on one or more of abio-information estimation formula according to an exemplary embodiment.For example, a bio-information estimate and a bio-information variationat the same point in time may be input to a predefined bio-informationcorrection formula to output corrected bio-information. In this case,examples of the bio-information correction formula are illustrated inEquation 1 or Equation 2.

In another example, the processor 810 may extract an outlier value thatis out of the confidence interval of the bio-information from among thebio-information estimates based on confidence interval information ofthe bio-information, and may replace the extracted outlier value with avalue within the confidence interval. For example, the processor 810 mayreplace the outlier value with a boundary value of the confidenceinterval (e.g., a value equal to or less than a maximum value of theconfidence interval or a value equal to or greater than a minimum valueof the confidence interval) at the same point in time. Alternatively,the processor 810 may correct the outlier value of a certain time pointusing an average or intermediate value of normal values (e.g., normalestimates of the bio-information or corrected values of outlier valuesor corrected values of missing values) of a preceding and a subsequenttime points, or an average or intermediate value of weighted normalvalues of a preceding and a subsequent time points, but the values to beused in correction are not particularly limited to the these examples.

In another example, the processor 810 may estimate a missing value byanalyzing the bio-information estimates and correct the missing valueusing the metabolic information. For example, as described withreference to Equation 3 or Table 1 above, the processor 810 may correctthe missing value using a variation of bio-information or probabilityinformation of each variation range.

When the estimation and correction of the bio-information is completed,the processor 810 may perform various actions based on the correctionresult. For example, the processor 810 may transmit the correctionresult to an external device that has requested the bio-informationestimation. In addition, the processor 810 may generate a graph showinga comparison between the bio-information estimation result and thecorrection result. Further, the processor 810 may monitor a healthstatus of the user based on the bio-information correction result todetermine whether the health status is normal, and generate alarm orwarning information to be provided to the user.

The processor 810 may compare the bio-information estimation result andthe correction result to determine whether to calibrate thebio-information estimation model. For example, the processor 810 mayobtain a difference between the bio-information estimate and thecorrected value at each point in time and when the number of occurrencesthat the difference exceeds a threshold meets a predetermined criterion,the processor 810 may determine to calibrate the bio-informationestimation model.

When the processor 810 determines to calibrate the bio-informationestimation model, the processor 810 may receive a bio-informationmeasurement from an external device, for example, a non-invasivebio-information measurement apparatus and calibrate the bio-informationestimation model.

The outputter 830 may provide a processing result of the processor 810to the user. For example, the outputter 830 may display thebio-information correction result on a display as final bio-informationor output the result in a sound and/or voice form. In addition, theoutputter 830 may display warning and/or alarm information and a graphshowing a comparison result of the bio-information estimate and thecorrected value on the display. Further, the outputter 830 may providethe warning and/or alarm information by generating vibration or tactilesensation. To this end, the outputter 830 may include a display, anaudio output device (e.g., speaker, audio jack, audio output device),and/or a haptic module (e.g., vibration motor). However, exemplaryembodiments are not limited to these examples and the outputter 830 maybe variously embodied in accordance with output methods.

FIG. 9 is a diagram illustrating a wearable device according to anexemplary embodiment. FIG. 9 shows a wearable device in the form of asmart watch that is worn on the user's wrist and implements thebio-information estimating apparatus of FIG. 8.

Referring to FIG. 9, the wearable device 900 includes a main body 910and a strap 920. The processor 810, the sensor 820, and the outputter830 of the bio-information estimating apparatus 800 shown in FIG. 8 maybe mounted inside the main body 910 or be mounted in such a manner thatthey are exposed to the outside.

The main body 910 may be worn on a user's wrist by the strap 920, andthe strap 920 may be connected to a first side and a second side of themain body 910 to be fastened to each other. The strap 920 may include aflexible member to wrap around the wrist.

A battery may be equipped in the main body 910 and/or the strap 920 tosupply power to the wearable device.

The wearable device 900 may include a spectroscopic sensor mounted inthe main body 910 and configured to measure a spectrum of the user'swrist part. The spectroscopic sensor may include a light source and adetector. The light source may be mounted on a lower part of the mainbody 910 and be exposed to the wrist so as to emit light to the wristpart of the user. The detector may include a photodiode and detect lightreturning from the user's skin to acquire a spectrum. However, thesensor to be mounted in the wearable device 900 is not limited to thespectroscopic sensor, as described above, and one or more variousnon-invasive sensors may be mounted in the main body 910 depending onembodiments.

The wearable device 900 may include a gyro sensor configured to acquireinclination information of the main body 910 and a camera module (e.g.,camera) configured to collect image information related to a type offood that the user is taking. In addition, the wearable device 900 mayinclude a communication module for communicating with the food intakesensor configured to acquire information about a food intake sound,recognition of neck swallowing and neck muscle movement, etc. while theuser is taking food.

The processor 810 mounted inside the main body 910 may receive a user'sinstruction input through an operator 915 and/or a display 914 andperform an operation in response to the received instruction. Forexample, the processor 810 may electrically connected to thespectroscopic sensor, generate a control signal for controlling thespectroscopic sensor when receiving a bio-information estimationinstruction from the user, and transmit the control signal to thespectroscopic sensor. When the spectroscopic sensor acquires spectralinformation, the processor 810 may receive the spectral information andestimate bio-information by using a bio-information estimation model.

In addition, when the user takes food, the processor 810 may receivesensor information related to user's food intake from the gyro sensor,the camera module, and the food intake sensor, and acquire the user'sfood intake information by analyzing the received sensor information. Inaddition, as described above, the processor 810 may analyze thebio-information measurements to acquire food intake information ordirectly receive the food intake information from the user.

The processor 810 may communicate with an external device by controllingthe communication module. The processor 810 may transmit thebio-information estimates and/or corrected values to the external deviceso that the external device can perform various functions related tomonitoring of a user's health status. The external device may be aninformation processing device, such as a smartphone, a tablet PC, adesktop PC, a notebook PC, which has a relatively excellent computingperformance.

The wearable device 900 may further include the display 914 mounted onan upper part of the main body 910 and configured to provide aprocessing result of the processor 810 to the user. For example, thedisplay 914 may output the corrected value of the bio-information asfinal bio-information. In addition, the display 914 may display a resultof comparison between the bio-information estimate and a corrected valueof the bio-information, warning and alarm information, and the like.Moreover, the display 914 may display an interface through which variousinstructions are received from or guided to the user, and may transmitinformation input through the interface to the processor 810. Thedisplay 914 may be formed as a module capable of touch input.

The wearable device 900 may further include the operator 915 mounted inthe main body 910. The operator 915 may be formed on one side of themain body 910 to be exposed to the outside, receive the instructioninput by the user, and transmit the instruction to the processor 810.The operator 915 may include a function of turning on/off the power ofthe wearable device.

FIG. 10 is a flowchart illustrating a method of estimatingbio-information according to an exemplary embodiment.

The method shown in FIG. 10 may be performed by the apparatus 800 forestimating bio-information illustrated in FIG. 8. Various exemplaryembodiments of the method of estimating bio-information has beendescribed above, and thus a brief description thereof will be givenbelow.

The apparatus 800 for estimating bio-information collects sensorinformation from a user in operation 1010. For example, spectralinformation may be acquired from the user through a spectroscopicsensor. In this case, the spectroscopic sensor may continuously acquirethe spectral information at predetermined time intervals. In addition,when the user takes food, food intake sensor information may becollected through a food intake sensor that recognizes the user's foodintake.

The apparatus 800 estimates bio-information based on the sensorinformation in operation 1020. The apparatus 800 may estimate thebio-information based on the spectral information acquired by thespectroscopic sensor and, at this time, may estimate the bio-informationusing a bio-information estimation model provided in advance.

In operation 1030, the apparatus 800 acquires the user's food intakeinformation using at least one of the food intake sensor informationcollected by the food intake sensor in operation 1010, the continuousbio-information estimates obtained in operation 1020, and the foodintake information input by the user.

In operation 1040, when the user's food intake information is acquired,the apparatus 800 extracts metabolic information by referring to aphysiological metabolic model or a bio-information database. In thiscase, the physiological metabolic model and the bio-information databasemay be personalized to each user and provided in advance, and mayinclude information indicating a correlation between the food intakeinformation, such as the type and amount of food taken by the user, thefood intake time, and the like, and the change in bio-information overtime.

In operation 1050, the apparatus 800 corrects the bio-informationestimated in operation 1020 using the extracted metabolic information.For example, the apparatus 800 may correct the bio-information estimatesobtained in operation 1020 based on a change amount over time of thebio-information. In addition, an outlier value is extracted from thebio-information estimates based on the confidence interval informationwith respect to time, and the extracted outlier value may be corrected.In addition, a missing value in an interval or at a point in time atwhich a bio-information estimate is missing is estimated and theestimated missing value is corrected based on a change amount over timeof the bio-information or the probability information of each variationrange.

In operation 1060, the apparatus 800 outputs the bio-informationcorrection result obtained in operation 1050. In addition, the apparatus800 may output a result of comparison between the bio-informationestimate and the corrected value or warning/alarm information. In thiscase, the apparatus 800 may visually output the information by varyingcolor, font size, or thickness, or may output the information using anon-visual means, such as a voice, tactile sensation, vibration, or thelike, or output the information using both a non-visual means and avisual means at the same time.

The exemplary embodiments can be implemented as computer readable codesin a computer readable record medium. Codes and code segmentsconstituting the computer program can be easily inferred by a skilledcomputer programmer in the art. The computer readable record mediumincludes all types of record media in which computer readable data arestored. Examples of the computer readable record medium include a ROM, aRAM, a CD-ROM, a magnetic tape, a floppy disk, and an optical datastorage. Further, the record medium may be implemented in the form of acarrier wave such as Internet transmission. In addition, the computerreadable record medium may be distributed to computer systems over anetwork, in which computer readable codes may be stored and executed ina distributed manner.

According to exemplary embodiments, when the bio-information is measuredby a bio-information sensor, it is possible to accurately measure thebio-information by correcting the bio-information measurements, theoutlier values and the missing values using the user's metabolicinformation.

At least one of the components, elements or units represented by a blockas illustrated in the drawings may be embodied as various numbers ofhardware, software and/or firmware structures that execute respectivefunctions described above, according to an exemplary embodiment. Forexample, at least one of these components, elements or units may use adirect circuit structure, such as a memory, processing, logic, a look-uptable, etc. that may execute the respective functions through controlsof one or more microprocessors or other control apparatuses. Also, atleast one of these components, elements or units may be specificallyembodied by a module, a program, or a part of code, which contains oneor more executable instructions for performing specified logicfunctions, and executed by one or more microprocessors or other controlapparatuses. Also, at least one of these components, elements or unitsmay further include a processor such as a central processing unit (CPU)that performs the respective functions, a microprocessor, or the like.Two or more of these components, elements or units may be combined intoone single component, element or unit which performs all operations orfunctions of the combined two or more components, elements of units.Also, at least part of functions of at least one of these components,elements or units may be performed by another of these components,element or units. Further, although a bus is not illustrated in theabove block diagrams, communication between the components, elements orunits may be performed through the bus. Functional aspects of the aboveexemplary embodiments may be implemented in algorithms that execute onone or more processors. Furthermore, the components, elements or unitsrepresented by a block or processing steps may employ any number ofrelated art techniques for electronics configuration, signal processingand/or control, data processing and the like.

A number of examples have been described above. Nevertheless, it will beunderstood that various modifications may be made. For example, suitableresults may be achieved if the described techniques are performed in adifferent order and/or if components in a described system,architecture, device, or circuit are combined in a different mannerand/or replaced or supplemented by other components or theirequivalents. Accordingly, other implementations are within the scope ofthe following claims.

What is claimed is:
 1. An apparatus for estimating bio-information,comprising: a sensor configured to obtain sensor information from auser; and a processor configured to estimate the bio-information basedon the sensor information, extract metabolic information based on foodintake information of the user, correct the estimated bio-informationusing the extracted metabolic information, and provide a result ofcorrecting the estimated bio-information.
 2. The apparatus of claim 1,wherein the sensor information comprises at least one from amongspectrometer measurement information, impedance measurement information,ultrasonic measurement information, thermal measurement information,electrocardiography (ECG) information, electroencephalogram (EEG)information, electromyography (EMG) information, electrooculography(EOG) information, and photoplethysmography (PPG) information.
 3. Theapparatus of claim 1, wherein the sensor comprises a food intake sensorconfigured to acquire food intake sensor information by detecting a foodintake of the user, and the processor is further configured to acquirethe food intake information based on the food intake sensor information.4. The apparatus of claim 1, wherein the processor is further configuredto extract the metabolic information by using at least one from among aphysiological metabolic model and a bio-information database.
 5. Theapparatus of claim 1, wherein the metabolic information comprises atleast one from among a change amount of the bio-information over time, aconfidence interval of the bio-information, and a probability of thechange amount being in a certain variation range.
 6. The apparatus ofclaim 5, wherein the processor is further configured to correct anestimate of the bio-information by using at least one from among acorrelation model and a correction formula indicating a correlationbetween the estimate of the bio-information and the change amount of thebio-information over time or the probability of the change amount beingin the certain variation range.
 7. The apparatus of claim 5, wherein theprocessor is further configured to correct an outlier value amongestimates of the bio-information which is out of the confidence intervalwith a value within the confidence interval.
 8. The apparatus of claim5, wherein the processor is further configured to determine an estimateof the bio-information as not being based on an actual measurement ofthe bio-information and correct the estimate based on at least one fromamong the change amount of the bio-information over time and theprobability of the change amount of the bio-information being in thecertain variation range.
 9. A method of estimating bio-information,comprising: obtaining sensor information from a user; estimatingbio-information based on the sensor information; acquiring food intakeinformation of the user; extracting metabolic information based on thefood intake information; and correcting the estimated bio-informationbased on the extracted metabolic information and providing a result ofcorrecting the estimated bio-information.
 10. The method of claim 9,wherein the sensor information comprises at least one from amongspectrometer measurement information, impedance measurement information,ultrasonic measurement information, thermal measurement information, ECGinformation, EEG information, EMG information, EOG information, and PPGinformation.
 11. The method of claim 9, wherein the obtaining the sensorinformation comprises obtaining food intake sensor information by usinga food intake sensor configured to detect a food intake of the user andthe acquiring the food intake information comprises acquiring the foodintake information of the user based on the food intake sensorinformation.
 12. The method of claim 9, wherein the extracting themetabolic information comprises extracting the metabolic information byusing at least one from among a physiological metabolic model and abio-information database.
 13. The method of claim 9, wherein thecorrecting the bio-information comprises at least one from amongcorrecting an outlier value which is out of a confidence interval of thebio-information and correcting an estimate of the receivedbio-information that is determined as not being based on an actualmeasurement of the bio-information.